标题:Robust Constrained Recursive Least P-Power Algorithm for Adaptive Filtering
作者:Sun, Jiajun ;Peng, Siyuan ;Liu, Qinglai ;Zhao, Ruijie ;Lin, Zhiping
作者机构:[Sun, Jiajun ;Peng, Siyuan ;Liu, Qinglai ;Lin, Zhiping ] School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; 更多
会议名称:23rd IEEE International Conference on Digital Signal Processing, DSP 2018
会议日期:19 November 2018 through 21 November 2018
来源:International Conference on Digital Signal Processing, DSP
出版年:2019
卷:2018-November
DOI:10.1109/ICDSP.2018.8631663
关键词:CRLP; LMP; non-Gaussian noises
摘要:In this paper, we develop a novel constrained adaptive filtering algorithm called constrained recursive least p-power (CRLP) algorithm, which incorporates a set of linear constraints into the least mean p-power error (LMP) criterion to solve a constrained optimization problem directly. Compared with the conventional constrained adaptive filtering algorithms including constrained least mean square (CLMS), constrained recursive least square (CRLS) and constrained least mean p-power (CLMP), CRLP can achieve better performance under non-Gaussian noises. Simulation results are presented to confirm the superior performance of the new algorithm. © 2018 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062801634&doi=10.1109%2fICDSP.2018.8631663&partnerID=40&md5=97f27100f9c52d453a87be6fa6a0d60d
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